






'''simple docstring'''





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
						import jax.numpy as jnp

						from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class  snake_case__      (					unittest.TestCase):




					@slow
					def 		A      (  self  :		Optional[Any]      )    ->							str:
								UpperCAmelCase_      :       str          =       FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''      )
								UpperCAmelCase_      :       Union[str, Any]          =       AutoTokenizer.from_pretrained('''xlm-roberta-base'''      )
								UpperCAmelCase_      :       Any          =       '''The dog is cute and lives in the garden house'''
								UpperCAmelCase_      :       Dict          =       jnp.array([tokenizer.encode(__snake_case      )]      )

								UpperCAmelCase_      :       Optional[Any]          =       (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase_      :       int          =       jnp.array(
								    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]      )

								UpperCAmelCase_      :       int          =       model(__snake_case      )['''last_hidden_state''']
								self.assertEqual(output.shape				, __snake_case      )
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]				, __snake_case				, atol=1e-3      )      )






from functools import lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2
     snake_case											=							set()
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.add(UpperCamelCase_							)
     if n > 1:
          factors.add(UpperCamelCase_							)
     return factors
@lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(unique_prime_factors(UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(set(UpperCamelCase_							)							) in (0, 1)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2

     while True:
          # Increment each value of a generated range
          snake_case											=							[base + i for i in range(UpperCamelCase_							)]

          # Run elements through out unique_prime_factors function
          # Append our target number to the end.
          snake_case											=							[upf_len(UpperCamelCase_							) for x in group]
          checker.append(UpperCamelCase_							)

          # If all numbers in the list are equal, return the group variable.
          if equality(UpperCamelCase_							):
               return group

          # Increment our base variable by 1
          base += 1
def      UpperCAmelCase__		(UpperCamelCase_ = 4							):



     """simple docstring"""






     snake_case											=							run(UpperCamelCase_							)
     return results[0] if len(UpperCamelCase_							) else None


if __name__ == "__main__":
 print(solution())




